Graph Generation for Drug Discovery using Python and Keras

Graph Generation for Drug Discovery using Python and Keras, available at $44.99, has an average rating of 4.15, with 37 lectures, based on 19 reviews, and has 207 subscribers.

You will learn about Understand the basics of graph generation and its applications in various fields. Learn how to manipulate molecular structures using the RDKit library in Python. Gain proficiency in preprocessing chemical data stored in CSV files. Develop an understanding of mapping atom symbols and bond types to numerical representations. Learn to convert SMILES strings into graph representations. Understand the concepts of Generative Adversarial Networks (GANs) and their application in graph generation. Implement a Graph Generator using TensorFlow and Keras to generate molecular graphs. Create a Discriminator model to evaluate the quality of generated graphs. Learn about the Wasserstein GAN framework for improved GAN training stability. Gain hands-on experience in training and fine-tuning GAN models for graph generation tasks. Understand the importance of GPU acceleration and how to configure it for faster computations. Develop the ability to save and load model weights for future use. Gain proficiency in generating molecular graphs using the trained GAN model. Learn to visualize and analyze the generated molecular structures. This course is ideal for individuals who are Beginners in Machine Learning: If you're new to the field of machine learning and want to learn how to generate molecular graphs using advanced techniques, this course will provide a gentle and comprehensive introduction. or Aspiring Data Scientists: If you're aspiring to become a data scientist or work in the domain of chemistry-related data analysis, this course will equip you with valuable skills in graph generation and neural networks. or Chemistry Enthusiasts: If you have a background or interest in chemistry and want to explore how machine learning can be applied to molecular structures and graph generation, this course will bridge the gap between chemistry and AI. or Python Programmers: If you are already familiar with Python programming and want to expand your knowledge into the realm of graph-based machine learning, this course will offer a structured pathway. or Students and Researchers: Whether you're a student working on a project or a researcher looking to integrate graph generation into your work, this course will provide practical skills and knowledge to enhance your capabilities. or Lifelong Learners: If you're simply curious about the intersection of machine learning, chemistry, and graph generation, this course welcomes learners of all backgrounds and experiences. It is particularly useful for Beginners in Machine Learning: If you're new to the field of machine learning and want to learn how to generate molecular graphs using advanced techniques, this course will provide a gentle and comprehensive introduction. or Aspiring Data Scientists: If you're aspiring to become a data scientist or work in the domain of chemistry-related data analysis, this course will equip you with valuable skills in graph generation and neural networks. or Chemistry Enthusiasts: If you have a background or interest in chemistry and want to explore how machine learning can be applied to molecular structures and graph generation, this course will bridge the gap between chemistry and AI. or Python Programmers: If you are already familiar with Python programming and want to expand your knowledge into the realm of graph-based machine learning, this course will offer a structured pathway. or Students and Researchers: Whether you're a student working on a project or a researcher looking to integrate graph generation into your work, this course will provide practical skills and knowledge to enhance your capabilities. or Lifelong Learners: If you're simply curious about the intersection of machine learning, chemistry, and graph generation, this course welcomes learners of all backgrounds and experiences.

Enroll now: Graph Generation for Drug Discovery using Python and Keras

Summary

Title: Graph Generation for Drug Discovery using Python and Keras

Price: $44.99

Average Rating: 4.15

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will Learn

• Understand the basics of graph generation and its applications in various fields.
• Learn how to manipulate molecular structures using the RDKit library in Python.
• Gain proficiency in preprocessing chemical data stored in CSV files.
• Develop an understanding of mapping atom symbols and bond types to numerical representations.
• Learn to convert SMILES strings into graph representations.
• Understand the concepts of Generative Adversarial Networks (GANs) and their application in graph generation.
• Implement a Graph Generator using TensorFlow and Keras to generate molecular graphs.
• Create a Discriminator model to evaluate the quality of generated graphs.
• Learn about the Wasserstein GAN framework for improved GAN training stability.
• Gain hands-on experience in training and fine-tuning GAN models for graph generation tasks.
• Understand the importance of GPU acceleration and how to configure it for faster computations.
• Develop the ability to save and load model weights for future use.
• Gain proficiency in generating molecular graphs using the trained GAN model.
• Learn to visualize and analyze the generated molecular structures.

Who Should Attend

• Beginners in Machine Learning: If you're new to the field of machine learning and want to learn how to generate molecular graphs using advanced techniques, this course will provide a gentle and comprehensive introduction.
• Aspiring Data Scientists: If you're aspiring to become a data scientist or work in the domain of chemistry-related data analysis, this course will equip you with valuable skills in graph generation and neural networks.
• Chemistry Enthusiasts: If you have a background or interest in chemistry and want to explore how machine learning can be applied to molecular structures and graph generation, this course will bridge the gap between chemistry and AI.
• Python Programmers: If you are already familiar with Python programming and want to expand your knowledge into the realm of graph-based machine learning, this course will offer a structured pathway.
• Students and Researchers: Whether you're a student working on a project or a researcher looking to integrate graph generation into your work, this course will provide practical skills and knowledge to enhance your capabilities.
• Lifelong Learners: If you're simply curious about the intersection of machine learning, chemistry, and graph generation, this course welcomes learners of all backgrounds and experiences.

Target Audiences

• Beginners in Machine Learning: If you're new to the field of machine learning and want to learn how to generate molecular graphs using advanced techniques, this course will provide a gentle and comprehensive introduction.
• Aspiring Data Scientists: If you're aspiring to become a data scientist or work in the domain of chemistry-related data analysis, this course will equip you with valuable skills in graph generation and neural networks.
• Chemistry Enthusiasts: If you have a background or interest in chemistry and want to explore how machine learning can be applied to molecular structures and graph generation, this course will bridge the gap between chemistry and AI.
• Python Programmers: If you are already familiar with Python programming and want to expand your knowledge into the realm of graph-based machine learning, this course will offer a structured pathway.
• Students and Researchers: Whether you're a student working on a project or a researcher looking to integrate graph generation into your work, this course will provide practical skills and knowledge to enhance your capabilities.
• Lifelong Learners: If you're simply curious about the intersection of machine learning, chemistry, and graph generation, this course welcomes learners of all backgrounds and experiences.